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When clinicians evaluate the complicated benefits, risks, and costs of alternative options in patient care, a method of easing the decisional process is offered by the formal quantitative strategy calleddecision analysis.1,2Many practicing clinicians, however, are uncomfortable with quantitative decision analysis, because its mathematical components differ substantially from the customary processes of clinical judgment. According to the quantitative model, the best decision in a particular clinical situation depends on an appropriately added series of individual probability-utility scores. The individual scores are formed when a probability value, which represents the estimated rate of occurrence for each possible outcome event, is multiplied by a utility value, which is an arbitrarily assigned numerical rating for the relative degree of joy, despair, or costs associated with the event. These composite scores are an unfamiliar quantitative element in clinical reasoning.3Clinicians are accustomed to thinking separately about individual probabilities for the possible
Alvan R. Feinstein (Mon,) studied this question.